Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adaptation. Their self-adaptive mutation control turned out to be exceptionally successful. But nevertheless self-adaptation has not achieved the attention it deserves.

This book introduces various types of self-adaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems. After the analysis of self-adaptive crossover operators the book concentrates on premature convergence of self-adaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.

This book discusses innovative methods for mining information from images of plants, especially leaves, and highlights the diagnostic features that can be implemented in fully automatic systems for identifying plant species. Adopting a multidisciplinary approach, it explores the problem of plant species identification, covering both the concepts of taxonomy and morphology. It then provides an overview of morphometrics, including the historical background and the main steps in the morphometric analysis of leaves together with a number of applications. The core of the book focuses on novel diagnostic methods for plant species identification developed from a computer scientist's perspective. It then concludes with a chapter on the characterization of botanists' visions, which highlights important cognitive aspects that can be implemented in a computer system to more accurately replicate the human expert's fixation process. The book not only represents an authoritative guide to advanced computational tools for plant identification, but provides experts in botany, computer science and pattern recognition with new ideas and challenges. As such it is expected to foster both closer collaborations and further technological developments in the emerging field of automatic plant identification.

This book presents techniques that enable mobile manipulation robots to autonomously adapt to new situations. Covers kinematic modeling and learning; self-calibration; tactile sensing and object recognition; imitation learning and programming by demonstration.

The rapidly growing amount of data, available from di?erent technologies in the ?eld of bio-sciences, high-energy physics, economy, climate analysis, and in several other scienti?c disciplines, requires a new generation of machine learning and statistical methods to deal with their complexity and hete- geneity. As data collections becomes easier, data analysis is required to be more sophisticated in order to extract useful information from the available data. Even if data can be represented in several ways, according to their structural characteristics, ranging from strings, lists, trees to graphs and other more complex data structures, in most applications they are typically represented as a matrix whose rows correspond to measurable characteristics called f- tures, attributes, variables, depending on the considered discipline and whose columns correspond to examples (cases, samples, patterns). In order to avoid confusion,we will talk about features and examples.In real-worldtasks,there canbe manymorefeatures than examples(cancer classi?cationbasedongene expressionlevels in bioinformatics) or there can be many more examples than features(intrusion detection in computer/networksecurity). In addition, each example can be either labeled or not. Attaching labels allows to distinguish members of the same class or group from members of other classes or groups. Hence, one can talk about supervised and unsupervised tasks that can be solved by machine learning methods. Since it is widely accepted that no single classi?er or clustering algorithm canbesuperiortotheothers,ensemblesofsupervisedandunsupervisedme- ods are gaining popularity. A typical ensemble includes a number of clas- ?ers/clustererswhosepredictionsarecombinedtogetheraccordingtoacertain rule, e.g. majority vote.

The book focuses on different variants of decision tree induction but also describes the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehensive review of many meta-learning approaches explored in the past described in literature, including for instance approaches that provide a ranking of algorithms. The approach described can be related to other work that exploits planning whose aim is to construct data mining workflows. The book stimulates interchange of ideas between different, albeit related, approaches.

This book presents recent research on Advanced Computing in Industrial Mathematics, which is one of the most prominent interdisciplinary areas and combines mathematics, computer science, scientific computations, engineering, physics, chemistry, medicine, etc. Further, the book presents the tools of Industrial Mathematics, which are based on mathematical models, and the corresponding computer codes, which are used to perform virtual experiments to obtain new data or to better understand the existing experimental results. The book gathers the peer-reviewed papers presented during the 10th Annual Meeting of the Bulgarian Section of SIAM (BGSIAM) from December 21 to 22, 2015 in Sofia, Bulgaria.

This thesis presents a novel neuro-fuzzy modeling approach for grasp neuroprostheses. At first, it offers a detailed study of discomfort due to the application of Functional Electrical Stimulation to the upper limb. Then, it discusses briefly previous methods to model hand movements induced by FES with the purpose of introducing the new modeling approach based on intelligent systems. This approach is thoroughly described in the book, together with the proposed application to induce hand and finger movements by means of a surface FES system based on multi-field electrodes. The validation tests, carried out on both healthy and neurologically impaired subjects, demonstrate the efficacy of the proposed modeling method. All in all, the book proposes an innovative system based on fuzzy neural networks that is expected to improve the design and validation of advanced control systems for non-invasive grasp neuroprostheses.

This monograph has arisen from the multidisciplinary research extending over biology, robotics and hybrid systems theory. It is inspired by modeling reactive behavior of the immune system cell population, where each cell is considered an independent agent. The authors formulate the optimal control of maximizing the probability of robotic presence in a given region and discuss the application of the Minimum Principle for partial differential equations to this problem.

In recent years computational intelligence has been extended by adding many other subdisciplines and this new field requires a series of challenging problems that will give it a sense of direction in order to ensure that research efforts are not wasted. This book written by top experts in computational intelligence provides such clear directions and a much-needed focus on the most important and challenging research issues.

This book constitutes the refereed proceedings of the Second IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2011, held in Costa de Caparica, Portugal, in February 2011. The 67 revised full papers were carefully selected from numerous submissions. They cover a wide spectrum of topics ranging from collaborative enterprise networks to microelectronics. The papers are organized in topical sections on collaborative networks, service-oriented systems, computational intelligence, robotic systems, Petri nets, sensorial and perceptional systems, sensorial systems and decision, signal processing, fault-tolerant systems, control systems, energy systems, electrical machines, and electronics.

In modern distributed systems, such as the Internet of Things or cloud computing, verifying their correctness is an essential aspect. This requires modeling approaches that reflect the natural characteristics of such systems: the locality of their components, autonomy of their decisions, and their asynchronous communication. However, most of the available verifiers are unrealistic because one or more of these features are not reflected. Accordingly, in this book we present an original formalism: the Integrated Distributed Systems Model (IMDS), which defines a system as two sets (states and messages), and a relation of the "actions" between these sets. The server view and the traveling agent's view of the system provide communication duality, while general temporal formulas for the IMDS allow automatic verification. The features that the model checks include: partial deadlock and partial termination, communication deadlock and resource deadlock. Automatic verification can support the rapid development of distributed systems. Further, on the basis of the IMDS, the Dedan tool for automatic verification of distributed systems has been developed.

This book is a collection of representative and novel works done in Data Mining, Knowledge Discovery, Clustering and Classification that were originally presented in French at the EGC'2013 (Toulouse, France, January 2013) and EGC'2014 Conferences (Rennes, France, January 2014). These conferences were respectively the 13th and 14th editions of this event, which takes place each year and which is now successful and well-known in the French-speaking community. This community was structured in 2003 by the foundation of the French-speaking EGC society (EGC in French stands for "Extraction et Gestion des Connaissances" and means "Knowledge Discovery and Management", or KDM). This book is aiming at all researchers interested in these fields, including PhD or MSc students, and researchers from public or private laboratories. It concerns both theoretical and practical aspects of KDM. The book is structured in two parts called "Applications of KDM to real datasets" and "Foundations of KDM".

This book comprises a selection of extended abstracts and papers presented at the EVOLVE 2012 held in Mexico City, Mexico. The aim of the EVOLVE is to build a bridge between probability, set oriented numerics, and evolutionary computation as to identify new common and challenging research aspects. The conference is also intended to foster a growing interest for robust and efficient methods with a sound theoretical background. EVOLVE aims to unify theory-inspired methods and cutting-edge techniques ensuring performance guarantee factors. By gathering researchers with different backgrounds, a unified view and vocabulary can emerge where the theoretical advancements may echo in different domains. Summarizing, the EVOLVE conference focuses on challenging aspects arising at the passage from theory to new paradigms and aims to provide a unified view while raising questions related to reliability, performance guarantees, and modeling. The extended papers of the EVOLVE 2012 make a contribution to this goal.

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Evolutionary scheduling is a vital research domain at the interface of artificial intelligence and operational research. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling. It demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.

The technique of data fusion has been used extensively in information retrieval due to the complexity and diversity of tasks involved such as web and social networks, legal, enterprise, and many others. This book presents both a theoretical and empirical approach to data fusion. Several typical data fusion algorithms are discussed, analyzed and evaluated. A reader will find answers to the following questions, among others:

What are the key factors that affect the performance of data fusion algorithms significantly?

What conditions are favorable to data fusion algorithms?

CombSum and CombMNZ, which one is better? and why?

What is the rationale of using the linear combination method?

How can the best fusion option be found under any given circumstances?"

Developments in the areas of biology and bioinformatics are continuously evolving and creating a plethora of data that needs to be analyzed and decrypted. Since it can be difficult to decipher the multitudes of data within these areas, new computational techniques and tools are being employed to assist researchers in their findings. The Handbook of Research on Computational Intelligence Applications in Bioinformatics examines emergent research in handling real-world problems through the application of various computation technologies and techniques. Featuring theoretical concepts and best practices in the areas of computational intelligence, artificial intelligence, big data, and bio-inspired computing, this publication is a critical reference source for graduate students, professionals, academics, and researchers.

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled DATA MINING: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

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This book presents advanced software development tools for construction, deployment and governance of Service Oriented Architecture (SOA) applications. Novel technical concepts and paradigms, formulated during the research stage and during development of such tools are presented and illustrated by practical usage examples. Hence this book will be of interest not only to theoreticians but also to engineers who cope with real-life problems. Additionally, each chapter contains an overview of related work, enabling comparison of the proposed concepts with exiting solutions in various areas of the SOA development process. This makes the book interesting also for students and scientists who investigate similar issues.

TheThird International Workshop on Multi-Robot Systems was held in March 2005 at the Naval Research Laboratory in Washington, D. C. , USA. Bringing together leading researchers and government sponsors for three days of technicalinterchange on multi-robot systems, theworkshop follows two previous highly successful gatherings in 2002 and 2003. Likethe previous two workshops, the meeting began with presentations byvarious government p- gram managers describing application areas and programs with an interest in multi-robot systems. U. S. Government representatives were on handfrom theOf?ce of Naval Research and several other governmental of?ces. Top - searchers inthe ?eld then presented their current activities in many areas of multi-robot systems. Presentations spannedawide rangeof topics, incl- ing task allocation, coordination in dynamicenvironments, information/sensor sharing andfusion, distributed mapping and coverage, motion planning and control, human-robot interaction, and applications of multi-robot systems. All presentations were given in a single-track workshop format. This proce- ings documents the work presented at the workshop. The research presen- tions were followed by panel discussions, in which all participants interacted to highlight the challenges of this ?eld and to develop possible solutions. In addition to the invited research talks, researchers and students were given an opportunity to present their work at poster sessions. We would like to thank the Naval Research Laboratory for sponsoring this workshop and providing the - cilitiesforthesemeetingstotakeplace. WeareextremelygratefultoMagdalena Bugajska, Paul Wiegand, and Mitchell A. Potter, for their vital help (and long hours) in editing these proceedings and to Michelle Caccivio for providing the administrative support to the workshop.

This book demonstrates how to describe and analyze a system's behavior and extract the desired prediction and control algorithms from this analysis. A typical prediction is based on observing similar situations in the past, knowing the outcomes of these past situations, and expecting that the future outcome of the current situation will be similar to these past observed outcomes. In mathematical terms, similarity corresponds to symmetry, and similarity of outcomes to invariance. This book shows how symmetries can be used in all classes of algorithmic problems of sciences and engineering: from analysis to prediction to control. Applications cover chemistry, geosciences, intelligent control, neural networks, quantum physics, and thermal physics. Specifically, it is shown how the approach based on symmetry and similarity can be used in the analysis of real-life systems, in the algorithms of prediction, and in the algorithms of control.

This book explains aspects of social networks, varying from development and application of new artificial intelligence and computational intelligence techniques for social networks to understanding the impact of social networks. Chapters 1 and 2 deal with the basic strategies towards social networks such as mining text from such networks and applying social network metrics using a hybrid approach; Chaps. 3 to 8 focus on the prime research areas in social networks: community detection, influence maximization and opinion mining. Chapter 9 to 13 concentrate on studying the impact and use of social networks in society, primarily in education, commerce, and crowd sourcing. The contributions provide a multidimensional approach, and the book will serve graduate students and researchers as a reference in computer science, electronics engineering, communications, and information technology.

Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures; the author uses non-graphical methods of their description, and takes an algebraic approach.

The monograph presents the methods of structural imsets and supermodular functions, and deals with independence implication and equivalence of structural imsets. Motivation, mathematical foundations and areas of application are included, and a rough overview of graphical methods is also given. In particular, the author has been careful to use suitable terminology, and presents the work so that it will be understood by both statisticians, and by researchers in artificial intelligence. The necessary elementary mathematical notions are recalled in an appendix.

Artificial intelligence provides an environmentally rich paradigm within which design research based on computational constructions can be carried out. This has been one of the foundations for the developing field called "design computing."

Recently, there has been a growing interest in what designers do when they design and how they use computational tools. This forms the basis of a newly emergent field called "design cognition" that draws partly on cognitive science.

This new conference series aims to provide a bridge between the two fields of "design computing" and "design cognition." The papers in this volume are from the "First International Conference on Design Computing and Cognition" (DCC'04) held at the Massachusetts Institute of Technology, USA. They represent state-of-the art research and development in design computing and cognition. They are of particular interest to researchers, developers and users of advanced computation in design and those who need to gain a better understanding of designing.

This volume is an initiative undertaken by the IEEE Computational Intelligence Society's Task Force on Security, Surveillance and Defense to consolidate and disseminate the role of CI techniques in the design, development and deployment of security and defense solutions. Applications range from the detection of buried explosive hazards in a battlefield to the control of unmanned underwater vehicles, the delivery of superior video analytics for protecting critical infrastructures or the development of stronger intrusion detection systems and the design of military surveillance networks. Defense scientists, industry experts, academicians and practitioners alike will all benefit from the wide spectrum of successful applications compiled in this volume. Senior undergraduate or graduate students may also discover uncharted territory for their own research endeavors.